


from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase   :    List[Any]     =		logging.get_logger(__name__)







class        __lowercase       (							_UpperCAmelCase  ):



		"""simple docstring"""

		UpperCamelCase     :					List[str]			       =		["pixel_values"]







		def __init__( self		,      A = True		,      A = 32		,      A=PILImageResampling.BILINEAR		,      A = True		,      **A		,      )  -> None:







					'''simple docstring'''







					lowerCamelCase    		=  do_resize
					lowerCamelCase    		=  do_rescale
					lowerCamelCase    		=  size_divisor
					lowerCamelCase    		=  resample
					super().__init__(**A	)







		def 					__A   ( self		,      A		,      A		,      A		,      A = None		,      **A	)  -> np.ndarray:







					'''simple docstring'''







					lowerCamelCase		,		lowerCamelCase    		=  get_image_size(A	)
					# Rounds the height and width down to the closest multiple of size_divisor
					lowerCamelCase    		=  height // size_divisor * size_divisor
					lowerCamelCase    		=  width // size_divisor * size_divisor
					lowerCamelCase    		=  resize(A		,      (new_h, new_w)		,      resample=A		,      data_format=A		,      **A	)
					return image







		def 					__A   ( self		,      A		,      A		,      A = None		,      **A	)  -> np.ndarray:







					'''simple docstring'''







					return rescale(image=A		,      scale=A		,      data_format=A		,      **A	)







		def 					__A   ( self		,      A		,      A = None		,      A = None		,      A=None		,      A = None		,      A = None		,      A = ChannelDimension.FIRST		,      **A		,      )  -> BatchFeature:







					'''simple docstring'''







					lowerCamelCase    		=  do_resize if do_resize is not None else self.do_resize
					lowerCamelCase    		=  do_rescale if do_rescale is not None else self.do_rescale
					lowerCamelCase    		=  size_divisor if size_divisor is not None else self.size_divisor
					lowerCamelCase    		=  resample if resample is not None else self.resample

					if do_resize and size_divisor is None:
								raise ValueError("""size_divisor is required for resizing"""	)

					lowerCamelCase    		=  make_list_of_images(A	)

					if not valid_images(A	):
								raise ValueError("""Invalid image(s)"""	)

					# All transformations expect numpy arrays.
					lowerCamelCase    		=  [to_numpy_array(A	) for img in images]

					if do_resize:
								lowerCamelCase    		=  [self.resize(A		,      size_divisor=A		,      resample=A	) for image in images]

					if do_rescale:
								lowerCamelCase    		=  [self.rescale(A		,      scale=1 / 2_55	) for image in images]

					lowerCamelCase    		=  [to_channel_dimension_format(A		,      A	) for image in images]

					lowerCamelCase    		=  {"""pixel_values""": images}
					return BatchFeature(data=A		,      tensor_type=A	)








"""simple docstring"""





class 						lowercase   :





      def __init__(			self					)						->	Any:
            lowerCAmelCase    		=			""""""
            lowerCAmelCase    		=			""""""
            lowerCAmelCase    		=			[]





      def        _snake_case     (			self ,		lowercase ,		lowercase					)						->	int:
            if m == -1:
                  return n + 1
            elif n == -1:
                  return m + 1
            elif self.dp[m][n] > -1:
                  return self.dp[m][n]
            else:
                  if self.worda[m] == self.worda[n]:
                        lowerCAmelCase    		=			self.__min_dist_top_down_dp(m - 1 ,		n - 1					)
                  else:
                        lowerCAmelCase    		=			self.__min_dist_top_down_dp(lowercase ,		n - 1					)
                        lowerCAmelCase    		=			self.__min_dist_top_down_dp(m - 1 ,		lowercase					)
                        lowerCAmelCase    		=			self.__min_dist_top_down_dp(m - 1 ,		n - 1					)
                        lowerCAmelCase    		=			1 + min(lowercase ,		lowercase ,		lowercase					)

                  return self.dp[m][n]





      def        _snake_case     (			self ,		lowercase ,		lowercase					)						->	int:
            lowerCAmelCase    		=			worda
            lowerCAmelCase    		=			worda
            lowerCAmelCase    		=			[[-1 for _ in range(len(lowercase					)					)] for _ in range(len(lowercase					)					)]

            return self.__min_dist_top_down_dp(len(lowercase					) - 1 ,		len(lowercase					) - 1					)





      def        _snake_case     (			self ,		lowercase ,		lowercase					)						->	int:
            lowerCAmelCase    		=			worda
            lowerCAmelCase    		=			worda
            lowerCAmelCase    		=			len(lowercase					)
            lowerCAmelCase    		=			len(lowercase					)
            lowerCAmelCase    		=			[[0 for _ in range(n + 1					)] for _ in range(m + 1					)]

            for i in range(m + 1					):
                  for j in range(n + 1					):
                        if i == 0:  # first string is empty
                              lowerCAmelCase    		=			j
                        elif j == 0:  # second string is empty
                              lowerCAmelCase    		=			i
                        elif worda[i - 1] == worda[j - 1]:  # last characters are equal
                              lowerCAmelCase    		=			self.dp[i - 1][j - 1]
                        else:
                              lowerCAmelCase    		=			self.dp[i][j - 1]
                              lowerCAmelCase    		=			self.dp[i - 1][j]
                              lowerCAmelCase    		=			self.dp[i - 1][j - 1]
                              lowerCAmelCase    		=			1 + min(lowercase ,		lowercase ,		lowercase					)
            return self.dp[m][n]


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__   =  EditDistance()

       print("****************** Testing Edit Distance DP Algorithm ******************")
       print()

       SCREAMING_SNAKE_CASE__   =  input("Enter the first string: ").strip()
       SCREAMING_SNAKE_CASE__   =  input("Enter the second string: ").strip()

       print()
       print(f'The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}')
       print(f'The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}')
       print()
       print("*************** End of Testing Edit Distance DP Algorithm ***************")






